{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# 文本预处理\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import re\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "将数据集读取到由多条文本行组成的列表中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "origin_pos": 5,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# text lines: 3221\n",
      "the time machine by h g wells\n",
      "twinkled and his usually pale face was flushed and animated the\n"
     ]
    }
   ],
   "source": [
    "d2l.DATA_HUB['time_machine'] = (d2l.DATA_URL + 'timemachine.txt',\n",
    "                                '090b5e7e70c295757f55df93cb0a180b9691891a')\n",
    "\n",
    "def read_time_machine():  \n",
    "    \"\"\"Load the time machine dataset into a list of text lines.\"\"\"\n",
    "    with open(d2l.download('time_machine'), 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]\n",
    "\n",
    "lines = read_time_machine()\n",
    "print(f'\n",
    "print(lines[0])\n",
    "print(lines[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "每个文本序列又被拆分成一个词元列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the', 'time', 'machine', 'by', 'h', 'g', 'wells']\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "['i']\n",
      "[]\n",
      "[]\n",
      "['the', 'time', 'traveller', 'for', 'so', 'it', 'will', 'be', 'convenient', 'to', 'speak', 'of', 'him']\n",
      "['was', 'expounding', 'a', 'recondite', 'matter', 'to', 'us', 'his', 'grey', 'eyes', 'shone', 'and']\n",
      "['twinkled', 'and', 'his', 'usually', 'pale', 'face', 'was', 'flushed', 'and', 'animated', 'the']\n"
     ]
    }
   ],
   "source": [
    "def tokenize(lines, token='word'):  \n",
    "    \"\"\"将文本行拆分为单词或字符词元。\"\"\"\n",
    "    if token == 'word':\n",
    "        return [line.split() for line in lines]\n",
    "    elif token == 'char':\n",
    "        return [list(line) for line in lines]\n",
    "    else:\n",
    "        print('错误：未知词元类型：' + token)\n",
    "\n",
    "tokens = tokenize(lines)\n",
    "for i in range(11):\n",
    "    print(tokens[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "构建一个字典，通常也叫做*词汇表*（vocabulary），用来将字符串类型的词元映射到从$0$开始的数字索引中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 9,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class Vocab:  \n",
    "    \"\"\"文本词汇表\"\"\"\n",
    "    def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):\n",
    "        if tokens is None:\n",
    "            tokens = []\n",
    "        if reserved_tokens is None:\n",
    "            reserved_tokens = []\n",
    "        counter = count_corpus(tokens)\n",
    "        self.token_freqs = sorted(counter.items(), key=lambda x: x[1],\n",
    "                                  reverse=True)\n",
    "        self.unk, uniq_tokens = 0, ['<unk>'] + reserved_tokens\n",
    "        uniq_tokens += [\n",
    "            token for token, freq in self.token_freqs\n",
    "            if freq >= min_freq and token not in uniq_tokens]\n",
    "        self.idx_to_token, self.token_to_idx = [], dict()\n",
    "        for token in uniq_tokens:\n",
    "            self.idx_to_token.append(token)\n",
    "            self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.idx_to_token)\n",
    "\n",
    "    def __getitem__(self, tokens):\n",
    "        if not isinstance(tokens, (list, tuple)):\n",
    "            return self.token_to_idx.get(tokens, self.unk)\n",
    "        return [self.__getitem__(token) for token in tokens]\n",
    "\n",
    "    def to_tokens(self, indices):\n",
    "        if not isinstance(indices, (list, tuple)):\n",
    "            return self.idx_to_token[indices]\n",
    "        return [self.idx_to_token[index] for index in indices]\n",
    "\n",
    "def count_corpus(tokens):  \n",
    "    \"\"\"统计词元的频率。\"\"\"\n",
    "    if len(tokens) == 0 or isinstance(tokens[0], list):\n",
    "        tokens = [token for line in tokens for token in line]\n",
    "    return collections.Counter(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "构建词汇表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 11,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('<unk>', 0), ('the', 1), ('i', 2), ('and', 3), ('of', 4), ('a', 5), ('to', 6), ('was', 7), ('in', 8), ('that', 9)]\n"
     ]
    }
   ],
   "source": [
    "vocab = Vocab(tokens)\n",
    "print(list(vocab.token_to_idx.items())[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "将每一条文本行转换成一个数字索引列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "origin_pos": 13,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "words: ['the', 'time', 'machine', 'by', 'h', 'g', 'wells']\n",
      "indices: [1, 19, 50, 40, 2183, 2184, 400]\n",
      "words: ['twinkled', 'and', 'his', 'usually', 'pale', 'face', 'was', 'flushed', 'and', 'animated', 'the']\n",
      "indices: [2186, 3, 25, 1044, 362, 113, 7, 1421, 3, 1045, 1]\n"
     ]
    }
   ],
   "source": [
    "for i in [0, 10]:\n",
    "    print('words:', tokens[i])\n",
    "    print('indices:', vocab[tokens[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "将所有功能打包到`load_corpus_time_machine`函数中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170580, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_corpus_time_machine(max_tokens=-1):  \n",
    "    \"\"\"返回时光机器数据集的词元索引列表和词汇表。\"\"\"\n",
    "    lines = read_time_machine()\n",
    "    tokens = tokenize(lines, 'char')\n",
    "    vocab = Vocab(tokens)\n",
    "    corpus = [vocab[token] for line in tokens for token in line]\n",
    "    if max_tokens > 0:\n",
    "        corpus = corpus[:max_tokens]\n",
    "    return corpus, vocab\n",
    "\n",
    "corpus, vocab = load_corpus_time_machine()\n",
    "len(corpus), len(vocab)"
   ]
  }
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